Side scan sonar imaging is one of the advanced methods for seabed study.In order to be utilized in other projects,such as ocean engineering,the image needs to be classified according to the distributions of different classes of seabed materials.In this paper,seabed image is classified according to BP neural network,and Genetic Algorithm is adopted in train network in this paper.The feature vectors are average intensity,six statistics of texture and two dimensions of fractal.It considers not only the spatial correlation between different pixels,but also the terrain coarseness.The texture is denoted by the statistics of the co-occurrence matrix.Double Blanket algorithm is used to calculate dimension.Because a uniform fractal may not be sufficient to describe a seafloor,two dimensions are calculated respectively by the upper blanket and the lower blanket.However,in sonar image,fractal has directivity,i.e.there are different dimensions in different direction.Dimensions are different in acrosstrack and alongtrack,so the average of four directions is used to solve this problem.Finally,the real data verify the algorithm.In this paper,one hidden layer including six nodes is adopted.The BP network is rapidly and accurately convergent through GA.Correct classification rate is 92.5% in the result.

This paper develops a deep learning classification method with fully-connected 8-layers characteristics to classification of coastal wetland based on CHRIS hyperspectral image. The method combined spectral feature and multi-spatial texture feature information has been applied in the Huanghe (Yellow) River Estuary coastal wetland. The results show that:(1) Based on testing samples, the DCNN model combined spectral feature and texture feature after K-L transformation appear high classification accuracy, which is up to 99%. (2) The accuracy by using spectral feature with all the texture feature is lower than that using spectral only and combing spectral and texture feature after K-L transformation. The DCNN classification accuracy using spectral feature and texture feature after K-L transformation was up to 99.38%, and the outperformed that of all the texture feature by 4.15%. (3) The classification accuracy of the DCNN method achieves better performance than other methods based on the whole validation image, with an overall accuracy of 84.64% and the Kappa coefficient of 0.80. (4) The developed DCNN model classification algorithm ensured the accuracy of all types is more balanced, and it also greatly improved the accuracy of tidal flat and farmland, while kept the classification accuracy of main types almost invariant compared to the shallow algorithms. The classification accuracy of tidal flat and farmland is up to 79.26% and 56.72% respectively based on the DCNN model. And it improves by about 2.51% and 10.6% compared with that of the other shallow classification methods.

The Jiaojiang Estuary is shallow,macro-tidal dominated and extremely turbid,with a larger variation of the freshwater discharge.The estuarine stratification and classification are analysed by using a set of field data observed in wet season.
In spring tide,the depth-mean peak tidal currents can reach 2 m/s.During flood tide the water column is vertitally homogeneous,but the horizontal salinity gradient is large and there is a fresh water front.A 1 m thick fluid mud layer capped by lutocline is formed when the tidal current is less than 0.3 m/s.As the low-salinity trapped in the fluid mud layer,underlying saltier water enhances vertical mixing when the fluid mud layer is eroded and the water column is only slightly stratified during ebb tide.

By using a method of boundary temperature index of seasons, a classification of hydrological climatic seasons in the China seas is made on the basis statistics of the sea surface air and water temperatures over the years. The results indicate that the assignment of hydrological seasons in the China seas differs with various sea areas. It may be divided into three climatic belts. In the temperate zone area, four seasons are clearly distinct with very long winter. While in the subtropical zone area, there is no winter throughout the year. The autumn is linked together with the spring, and the summer is unusually long. As for the tropical zone area, it is summer all the year round without any other seasons. In addition, the regular pattern of transformation of the four seasons and the regional characteristics of the length of each season are analyzed in greater detail. The results are in agreement with the continental seasonal classification and it is also shown that the results are reasonable and reliable through test and verification.

In this paper, the principle and steps for differentiating water masses by fuzzy cluster method are introduced, and a scalar formula based on Euclidean distance and a method for determining objectively the number of water masses by F-test are proposed.Consequently, a method and specific steps for differentiating modified water masses in shallow sea according to fuzzy elastic classification are given.Computation of the membership degree in which each sample belongs to every water mass determines conveniently and quantitatively the cores, boundaries of water masses and mixed zones.An example for the Huanghai Sea and East China Sea is shown and compared with previous results.

Accurate detection of an oil spill is of great significance for rapid response to oil spill accidents. Multispectral images have the advantages of high spatial resolution, short revisit period, and wide imaging width, which is suitable for large-scale oil spill monitoring. However, in wide remote sensing images, the number of oil spill samples is generally far less than that of seawater samples. Moreover, the sea surface state tends to be heterogeneous over a large area, which makes the identification of oil spills more difficult because of various sea conditions and sunglint. To address this problem, we used the F-Score as a measure of the distance between forecast value and true value, proposed the Class-Balanced F loss function (CBF loss function) that comprehensively considers the precision and recall, and rebalances the loss according to the actual sample numbers of various classes. Using the CBF loss function, we constructed convolution neural networks (CBF-CNN) for oil spill detection. Based on the image acquired by the Coastal Zone Imager (CZI) of the Haiyang-1C (HY-1C) satellite in the Andaman Sea (study area 1), we carried out parameter adjustment experiments. In contrast to experiments of different loss functions, the F1-Score of the detection result of oil emulsions is 0.87, which is 0.03–0.07 higher than cross-entropy, hinge, and focal loss functions, and the F1-Score of the detection result of oil slicks is 0.94, which is 0.01–0.09 higher than those three loss functions. In comparison with the experiment of different methods, the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.05–0.12 higher than that of the deep neural networks, supports vector machine and random forests models, and the F1-Score of the detection result of oil slicks is 0.15–0.22 higher than that of the three methods. To verify the applicability of the CBF-CNN model in different observation scenes, we used the image obtained by HY-1C CZI in the Karimata Strait to carry out experiments, which include two studies areas (study area 2 and study area 3). The experimental results show that the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.88, which is 0.16–0.24 higher than that of other methods, and the F1-Score of the detection result of oil slicks is 0.96–0.97, which is 0.06–0.23 higher than that of other methods. Based on all the above experiments, we come to the conclusions that the CBF loss function can restrain the influence of oil spill and seawater sample imbalance on oil spill detection of CNN model thus improving the detection accuracy of oil spills, and our CBF-CNN model is suitable for the detection of oil spills in an area with weak sunglint and can be applied to different scenarios of CZI images.

The C-band synthetic aperture radar (SAR) data from the Bohai Sea of China, the Labrador Sea in the Arctic and the Weddell Sea in the Antarctic are used to analyze and discuss the sea ice full polarimetric information reconstruction ability under compact polarimetric modes. The type of compact polarimetric mode which has the highest reconstructed accuracy is analyzed, along with the performance impact of the reconstructed pseudo quad-pol SAR data on the sea ice detection and sea ice classification. According to the assessment and analysis, it is recommended to adopt the CTLR mode for reconstructing the polarimetric parameters σ_HH⁰,σ_W⁰,H,and α, while for reconstructing the polarimetric parameters σ_HV⁰,ρH-V,λ₁ and λ₂, it is recommended to use the π/4 mode. Moreover, it is recommended to use the π/4 mode in studying the action effects between the electromagnetic waves and sea ice, but it is recommended to use the CTLR mode for studying the sea ice classification.

Piezocone penetration test (CPTu), the preferred in-situ tool for submarine investigation, is significant for soil classification and soil depth profile prediction, which can be used to predict soil types and states. However, the accuracy of these methods needs to be validated for local conditions. To distinguish and evaluate the properties of the shallow surface sediments in Chengdao area of the Yellow River Delta, seabed CPTu tests were carried out at ten stations in this area. Nine soil classification methods based on CPTu data are applied for soil classification. The results of classification are compared with the in-situ sampling to determine whether the method can provide sufficient resolution. The methods presented by Robertson (based on soil behavior type index I_c), Olsen and Mitchell are the more consistent and compatible ones compared with other methods. Considering that silt soils have potential to liquefy under storm tide or other adverse conditions, this paper is able to screen soil classification methods suitable for the Chengdao area and help identify the areas where liquefaction or submarine landslide may occur through CPTu investigation.

In this paper,by using ISODATA of fuzzy cluster,the water masses classification of the upper layer in the E-quatorial Western Pacific is carried out.On the basis of the degree of the membership in the obtained optimal classification matrix,the solid distribution of the detailed structure of water masses is made.The water of the upper layer,consisting of six water masses,may be divided into three layers,i,e.,the surface,subsurface and intermediate layer.Besides analyzing the features of various water masses,a discussion on their distribution structure and formation mechanism is also made.

Given the advances in satellite altimetry and multibeam bathymetry, benthic terrain classification based on digital bathymetric models (DBMs) has been widely used for the mapping of benthic topographies. For instance, cobalt-rich crusts (CRCs) are important mineral resources found on seamounts and guyots in the western Pacific Ocean. Thick, plate-like CRCs are known to form on the summit and slopes of seamounts at the 1 000–3 000 m depth, while the relationship between seamount topography and spatial distribution of CRCs remains unclear. The benthic terrain classification of seamounts can solve this problem, thereby, facilitating the rapid exploration of seamount CRCs. Our study used an EM122 multibeam echosounder to retrieve high-resolution bathymetry data in the CRCs contract license area of China, i.e., the Jiaxie Guyots in 2015 and 2016. Based on the DBM construted by bathymetirc data, broad- and fine-scale bathymetric position indices were utilized for quantitative classification of the terrain units of the Jiaxie Guyots on multiple scales. The classification revealed four first-order terrain units (e.g., flat, crest, slope, and depression) and eleven second-order terrain units (e.g., local crests, depressions on crests, gentle slopes, crests on slopes, and local depressions, etc.). Furthermore, the classification of the terrain and geological analysis indicated that the Weijia Guyot has a large flat summit, with local crests at the southern summit, whereas most of the guyot flanks were covered by gentle slopes. “Radial” mountain ridges have developed on the eastern side, while large-scale gravitational landslides have developed on the western and southern flanks. Additionally, landslide masses can be observed at the bottom of these slopes. The coverage of local crests on the seamount is ~1 000 km², and the local crests on the peak and flanks of the guyots may be the areas where thick and continuous plate-like CRCs are likely to occur.